Pytorch Train: let users specify model inputs as args instead of kwargs

gunpowder/torch/nodes/train.py

@@ -29,7 +29,7 @@ class Train(GenericTrain):
 
             The torch optimizer to use.
 
-        inputs (``dict``, ``string`` -> :class:`ArrayKey`):
+        inputs (``dict``, ``string`` or ``int`` -> :class:`ArrayKey`):
 
             Dictionary from the names of input tensors (argument names of the
             ``forward`` method) in the model to array keys.
@@ -92,7 +92,7 @@ def __init__(
         model,
         loss,
         optimizer,
-        inputs: Dict[str, ArrayKey],
+        inputs: Dict[Union[str, int], ArrayKey],
         outputs: Dict[Union[int, str], ArrayKey],
         loss_inputs: Dict[Union[int, str], ArrayKey],
         gradients: Dict[Union[int, str], ArrayKey] = {},
@@ -112,11 +112,11 @@ def __init__(
 
         # not yet implemented
         gradients = gradients
-        all_inputs = {
-            k: v
-            for k, v in itertools.chain(inputs.items(), loss_inputs.items())
-            if v not in outputs.values()
-        }
+        loss_inputs = {f"loss_{k}": v for k, v in loss_inputs.items()}
+        all_inputs = {f"{k}": v for k, v in inputs.items() if v not in outputs.values()}
+        all_inputs.update(
+            {k: v for k, v in loss_inputs.items() if v not in outputs.values()}
+        )
 
         super(Train, self).__init__(
             all_inputs,
@@ -208,16 +208,22 @@ def start(self):
 
     def train_step(self, batch, request):
         inputs = self.__collect_provided_inputs(batch)
+        inputs = {k: torch.as_tensor(v, device=self.device) for k, v in inputs.items()}
         requested_outputs = self.__collect_requested_outputs(request)
 
         # keys are argument names of model forward pass
-        device_inputs = {
-            k: torch.as_tensor(v, device=self.device) for k, v in inputs.items()
-        }
+        device_input_args = []
+        for i in range(len(inputs)):
+            key = f"{i}"
+            if key in inputs:
+                device_input_args.append(inputs.pop(key))
+            else:
+                break
+        device_input_kwargs = {k: v for k, v in inputs.items() if isinstance(k, str)}
 
         # get outputs. Keys are tuple indices or model attr names as in self.outputs
         self.optimizer.zero_grad()
-        model_outputs = self.model(**device_inputs)
+        model_outputs = self.model(*device_input_args, **device_input_kwargs)
         if isinstance(model_outputs, tuple):
             outputs = {i: model_outputs[i] for i in range(len(model_outputs))}
         elif isinstance(model_outputs, torch.Tensor):
@@ -247,8 +253,9 @@ def train_step(self, batch, request):
 
         device_loss_args = []
         for i in range(len(device_loss_inputs)):
-            if i in device_loss_inputs:
-                device_loss_args.append(device_loss_inputs.pop(i))
+            key = f"loss_{i}"
+            if key in device_loss_inputs:
+                device_loss_args.append(device_loss_inputs.pop(key))
             else:
                 break
         device_loss_kwargs = {}
@@ -327,7 +334,12 @@ def __collect_requested_outputs(self, request):
 
     def __collect_provided_inputs(self, batch):
         return self.__collect_provided_arrays(
-            {k: v for k, v in self.inputs.items() if k not in self.loss_inputs}, batch
+            {
+                k: v
+                for k, v in self.inputs.items()
+                if (isinstance(k, int) or k not in self.loss_inputs)
+            },
+            batch,
         )
 
     def __collect_provided_loss_inputs(self, batch):
@@ -353,8 +365,9 @@ def __collect_provided_arrays(self, reference, batch, expect_missing_arrays=Fals
                 arrays[array_name] = getattr(batch, array_key)
             else:
                 raise Exception(
-                    "Unknown network array key {}, can't be given to "
-                    "network".format(array_key)
+                    "Unknown network array key {}, can't be given to " "network".format(
+                        array_key
+                    )
                 )
 
         return arrays